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The Volume-limited A-Star (VAST) survey - I. Companions and the unexpected X-ray detection of B6-A7 stars

2011· article· en· W1910381921 on OpenAlexaff
Robert J. De Rosa, J. Bulger, J. Patience, B. Leland, Bruce Macintosh, Adam C. Schneider, Inseok Song, Christian Marois, James R. Graham, M. S. Bessell, René Doyon

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de MontréalCanadian Institute for Theoretical AstrophysicsUniversity of TorontoHerzberg Institute of Astrophysics
FundersScience and Technology Facilities Council
KeywordsROSATPhysicsStarsAstrophysicsSkyAstronomyStellar classificationGalaxy

Abstract

fetched live from OpenAlex

With an adaptive optics imaging survey of 148 B6–A7 stars, we have tested the hypothesis that unresolved lower mass companions are the source of the unexpected X-ray detections of stars in this spectral type range. The sample is composed of 63 stars detected in X-rays within the ROSAT All Sky Survey and 85 stars that form a control sample; both subsets have the same restricted distribution of spectral type, age, X-ray sensitivity and separation coverage. A total of 68 companion candidates are resolved with separations ranging from 0.3 to 26.2 arcsec, with 23 new detections. The multiple star frequency of the X-ray sample based on companions resolved within the ROSAT error ellipse is found to be 43+6−6 per cent. The corresponding control sample multiple star frequency is three times lower at 12+4−3 per cent – a difference of 31 ± 7 per cent. These results are presented in the first of a series of papers based on our Volume-limited A-Star (VAST) survey – a comprehensive study of the multiplicity of A-type stars.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.193
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations228
Published2011
Admission routes1
Has abstractyes

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